Survival analysis with R for public health-Coursera
Rozen
2022-07-07
Read CSV file
library(readr)
g=read.csv("rawdata.csv")Checking the data
dim(g)## [1] 1000 31head(g)## id death los age gender cancer cabg crt defib dementia diabetes hypertension
## 1 1 0 2 90 2 0 0 0 0 0 0 0
## 2 2 0 10 74 1 0 0 0 0 0 0 1
## 3 3 0 3 83 2 0 0 0 0 0 0 1
## 4 4 0 1 79 1 0 0 0 0 0 1 1
## 5 5 0 17 94 2 0 0 0 0 0 1 1
## 6 6 0 47 89 1 0 0 0 0 0 0 0
## ihd mental_health arrhythmias copd obesity pvd renal_disease valvular_disease
## 1 0 0 1 0 0 0 0 1
## 2 1 0 0 0 0 0 1 1
## 3 0 0 1 0 0 0 0 0
## 4 1 0 0 1 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 1 0 1
## metastatic_cancer pacemaker pneumonia prior_appts_attended prior_dnas pci
## 1 0 0 0 4 0 0
## 2 0 0 1 9 1 0
## 3 0 0 0 1 0 0
## 4 0 1 0 9 2 1
## 5 0 0 0 3 0 0
## 6 0 0 1 3 0 0
## stroke senile quintile ethnicgroup fu_time
## 1 0 0 2 NA 416
## 2 0 0 4 1 648
## 3 0 0 3 1 466
## 4 1 0 5 1 441
## 5 0 0 2 1 371
## 6 0 0 3 NA 47tail(g)## id death los age gender cancer cabg crt defib dementia diabetes
## 995 995 1 3 80 1 0 0 0 0 0 0
## 996 996 0 8 73 1 0 0 0 0 0 0
## 997 997 1 5 84 2 0 0 0 0 0 0
## 998 998 0 3 87 1 0 0 0 0 0 0
## 999 999 1 62 86 2 0 0 0 0 0 0
## 1000 1000 1 8 75 1 0 0 0 0 0 0
## hypertension ihd mental_health arrhythmias copd obesity pvd renal_disease
## 995 1 1 0 1 0 1 1 1
## 996 1 1 0 0 1 0 0 1
## 997 1 1 0 0 1 0 0 1
## 998 1 1 0 0 0 0 1 1
## 999 1 0 0 0 1 0 0 0
## 1000 1 0 0 0 1 0 0 1
## valvular_disease metastatic_cancer pacemaker pneumonia
## 995 1 0 0 1
## 996 0 0 0 0
## 997 0 0 0 0
## 998 0 0 0 0
## 999 0 0 0 1
## 1000 0 0 0 1
## prior_appts_attended prior_dnas pci stroke senile quintile ethnicgroup
## 995 5 1 0 0 0 4 1
## 996 0 0 0 0 0 5 1
## 997 9 0 0 0 0 3 1
## 998 3 0 0 0 0 4 1
## 999 1 0 0 0 0 2 1
## 1000 5 0 0 0 0 5 1
## fu_time
## 995 445
## 996 351
## 997 1030
## 998 15
## 999 339
## 1000 8Library include
library(ggplot2)
library(survival)
library(Rcpp)
library(survminer)## Loading required package: ggpubr##
## Attaching package: 'survminer'## The following object is masked from 'package:survival':
##
## myelomaData conversion
gender=as.factor(g[,"gender"])
fu_time=as.numeric(g[,"fu_time"])
death=as.factor(g[,"death"])Kuplan-Meir plot
km_fit=survfit(Surv(fu_time,death) ~ 1)
plot(km_fit)
summary(km_fit,times = c(1:7,30.60,90*(1:10)))## Call: survfit(formula = Surv(fu_time, death) ~ 1)
##
## time n.risk n.event P((s0)) P(1)
## 1.0 992 12 0.988 0.0120
## 2.0 973 7 0.981 0.0192
## 3.0 963 5 0.976 0.0242
## 4.0 954 6 0.970 0.0304
## 5.0 945 5 0.964 0.0355
## 6.0 938 1 0.963 0.0365
## 7.0 933 1 0.962 0.0376
## 30.6 863 39 0.921 0.0785
## 90.0 770 52 0.864 0.1356
## 180.0 698 43 0.815 0.1850
## 270.0 653 24 0.787 0.2135
## 360.0 619 21 0.761 0.2391
## 450.0 525 44 0.705 0.2952
## 540.0 429 47 0.639 0.3611
## 630.0 362 32 0.589 0.4108
## 720.0 266 43 0.514 0.4858
## 810.0 190 31 0.448 0.5521
## 900.0 126 26 0.378 0.6222Kaplan-Meir with gender
Km_gender_fit=survfit(Surv(fu_time,death)~gender)
plot(Km_gender_fit)
### log rank test with gender
survdiff(Surv(fu_time,death)~gender, data = g)## Call:
## survdiff(formula = Surv(fu_time, death) ~ gender, data = g)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## gender=1 548 268 271 0.0365 0.082
## gender=2 452 224 221 0.0448 0.082
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.8summarize data
summary(g)## id death los age
## Min. : 1.0 Min. :0.000 Min. : 0.00 Min. : 29.00
## 1st Qu.: 250.8 1st Qu.:0.000 1st Qu.: 3.00 1st Qu.: 73.00
## Median : 500.5 Median :0.000 Median : 7.00 Median : 80.00
## Mean : 500.5 Mean :0.492 Mean :10.77 Mean : 78.73
## 3rd Qu.: 750.2 3rd Qu.:1.000 3rd Qu.:13.00 3rd Qu.: 87.00
## Max. :1000.0 Max. :1.000 Max. :89.00 Max. :102.00
##
## gender cancer cabg crt
## Min. :1.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :1.000 Median :0.000 Median :0.000 Median :0.000
## Mean :1.452 Mean :0.051 Mean :0.014 Mean :0.003
## 3rd Qu.:2.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :2.000 Max. :1.000 Max. :1.000 Max. :1.000
##
## defib dementia diabetes hypertension
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :1.000
## Mean :0.006 Mean :0.045 Mean :0.283 Mean :0.621
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
##
## ihd mental_health arrhythmias copd obesity
## Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.00 Median :0.000 Median :0.000
## Mean :0.495 Mean :0.089 Mean :0.49 Mean :0.242 Mean :0.058
## 3rd Qu.:1.000 3rd Qu.:0.000 3rd Qu.:1.00 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.00 Max. :1.000 Max. :1.000
##
## pvd renal_disease valvular_disease metastatic_cancer
## Min. :0.0 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.0 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.0 Median :0.000 Median :0.000 Median :0.000
## Mean :0.1 Mean :0.249 Mean :0.244 Mean :0.009
## 3rd Qu.:0.0 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.0 Max. :1.000 Max. :1.000 Max. :1.000
##
## pacemaker pneumonia prior_appts_attended prior_dnas
## Min. :0.000 Min. :0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 1.000 1st Qu.: 0.000
## Median :0.000 Median :0.000 Median : 3.000 Median : 0.000
## Mean :0.037 Mean :0.117 Mean : 5.538 Mean : 0.501
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.: 8.000 3rd Qu.: 1.000
## Max. :1.000 Max. :1.000 Max. :62.000 Max. :10.000
##
## pci stroke senile quintile ethnicgroup
## Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.00 Min. :1.000
## 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:2.00 1st Qu.:1.000
## Median :0.000 Median :0.00 Median :0.000 Median :3.00 Median :1.000
## Mean :0.029 Mean :0.02 Mean :0.044 Mean :3.16 Mean :1.231
## 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:4.00 3rd Qu.:1.000
## Max. :1.000 Max. :1.00 Max. :1.000 Max. :5.00 Max. :9.000
## NA's :6 NA's :43
## fu_time
## Min. : 0.0
## 1st Qu.: 117.8
## Median : 475.0
## Mean : 466.0
## 3rd Qu.: 743.2
## Max. :1107.0
## Age variable
age=g[,"age"]
age=as.numeric(g[,"age"])Age categorized
age_cat=ifelse(age<=65,"Below 65" ,ifelse(age>65,"Above 65",NA))
table(age_cat)## age_cat
## Above 65 Below 65
## 869 131Kaplan Meir with Age group
Km_age_fit=survfit(Surv(fu_time,death)~age_cat)
plot(Km_age_fit)
summary(Km_age_fit)## Call: survfit(formula = Surv(fu_time, death) ~ age_cat)
##
## age_cat=Above 65
## time n.risk n.event P((s0)) P(1)
## 0 869 3 0.9965 0.00345
## 1 864 9 0.9862 0.01383
## 2 846 7 0.9780 0.02199
## 3 836 5 0.9722 0.02784
## 4 827 6 0.9651 0.03490
## 5 820 5 0.9592 0.04078
## 6 814 1 0.9580 0.04196
## 7 809 1 0.9569 0.04314
## 8 805 4 0.9521 0.04790
## 10 800 1 0.9509 0.04909
## 11 797 4 0.9461 0.05386
## 12 790 3 0.9425 0.05745
## 13 784 3 0.9389 0.06106
## 14 781 3 0.9353 0.06467
## 15 777 1 0.9341 0.06587
## 16 775 1 0.9329 0.06708
## 17 772 2 0.9305 0.06949
## 18 769 1 0.9293 0.07070
## 19 768 3 0.9257 0.07433
## 20 764 1 0.9245 0.07554
## 23 760 3 0.9208 0.07919
## 25 755 1 0.9196 0.08041
## 26 754 3 0.9159 0.08407
## 27 751 1 0.9147 0.08529
## 28 749 1 0.9135 0.08651
## 29 748 1 0.9123 0.08773
## 30 747 1 0.9110 0.08895
## 31 745 3 0.9074 0.09262
## 33 736 2 0.9049 0.09509
## 34 734 1 0.9037 0.09632
## 36 732 1 0.9024 0.09756
## 37 729 1 0.9012 0.09879
## 38 728 2 0.8987 0.10127
## 41 721 2 0.8962 0.10376
## 43 718 1 0.8950 0.10501
## 48 713 3 0.8912 0.10878
## 49 710 1 0.8900 0.11003
## 50 708 1 0.8887 0.11129
## 53 705 1 0.8874 0.11255
## 54 704 2 0.8849 0.11507
## 57 701 3 0.8811 0.11886
## 58 698 1 0.8799 0.12012
## 59 696 2 0.8774 0.12265
## 61 694 2 0.8748 0.12518
## 62 692 1 0.8736 0.12644
## 63 691 1 0.8723 0.12771
## 64 690 1 0.8710 0.12897
## 65 689 1 0.8698 0.13023
## 67 687 1 0.8685 0.13150
## 72 686 1 0.8672 0.13277
## 73 685 1 0.8660 0.13403
## 76 682 1 0.8647 0.13530
## 77 680 1 0.8634 0.13657
## 79 678 2 0.8609 0.13912
## 80 675 2 0.8583 0.14167
## 82 672 1 0.8571 0.14295
## 83 670 1 0.8558 0.14423
## 85 669 1 0.8545 0.14551
## 86 667 3 0.8506 0.14935
## 87 664 2 0.8481 0.15191
## 95 656 1 0.8468 0.15321
## 97 653 2 0.8442 0.15580
## 98 651 1 0.8429 0.15710
## 99 650 1 0.8416 0.15839
## 103 648 1 0.8403 0.15969
## 104 647 1 0.8390 0.16099
## 109 645 1 0.8377 0.16229
## 110 643 1 0.8364 0.16359
## 115 642 1 0.8351 0.16490
## 117 641 1 0.8338 0.16620
## 118 639 1 0.8325 0.16750
## 119 637 1 0.8312 0.16881
## 121 635 1 0.8299 0.17012
## 125 634 1 0.8286 0.17143
## 126 633 1 0.8273 0.17274
## 127 632 1 0.8260 0.17405
## 134 626 1 0.8246 0.17537
## 135 625 2 0.8220 0.17801
## 137 622 1 0.8207 0.17933
## 141 620 1 0.8193 0.18065
## 142 619 2 0.8167 0.18330
## 148 615 1 0.8154 0.18463
## 149 614 1 0.8140 0.18595
## 152 612 1 0.8127 0.18728
## 153 611 1 0.8114 0.18861
## 157 608 1 0.8101 0.18995
## 158 607 2 0.8074 0.19262
## 160 605 1 0.8060 0.19395
## 165 603 1 0.8047 0.19529
## 167 601 1 0.8034 0.19663
## 170 600 2 0.8007 0.19931
## 177 597 1 0.7994 0.20065
## 178 596 1 0.7980 0.20199
## 180 593 2 0.7953 0.20468
## 186 590 1 0.7940 0.20603
## 192 588 1 0.7926 0.20738
## 193 587 1 0.7913 0.20873
## 194 586 1 0.7899 0.21008
## 195 585 2 0.7872 0.21278
## 197 583 1 0.7859 0.21413
## 199 581 1 0.7845 0.21548
## 201 580 1 0.7832 0.21683
## 206 579 1 0.7818 0.21819
## 212 577 1 0.7805 0.21954
## 213 576 1 0.7791 0.22090
## 214 575 1 0.7777 0.22225
## 217 574 1 0.7764 0.22361
## 220 573 1 0.7750 0.22496
## 229 570 1 0.7737 0.22632
## 231 568 1 0.7723 0.22768
## 237 566 1 0.7710 0.22905
## 238 564 1 0.7696 0.23042
## 249 561 1 0.7682 0.23179
## 252 560 1 0.7668 0.23316
## 256 559 1 0.7655 0.23453
## 261 556 1 0.7641 0.23591
## 264 555 1 0.7627 0.23728
## 277 551 1 0.7613 0.23867
## 287 549 1 0.7599 0.24006
## 291 547 1 0.7586 0.24144
## 294 545 1 0.7572 0.24284
## 295 544 1 0.7558 0.24423
## 296 542 1 0.7544 0.24562
## 297 541 1 0.7530 0.24702
## 300 539 1 0.7516 0.24841
## 307 538 1 0.7502 0.24981
## 308 537 1 0.7488 0.25121
## 310 536 1 0.7474 0.25260
## 314 534 1 0.7460 0.25400
## 316 533 1 0.7446 0.25540
## 320 532 1 0.7432 0.25680
## 321 531 1 0.7418 0.25820
## 324 530 1 0.7404 0.25960
## 339 528 1 0.7390 0.26101
## 343 527 1 0.7376 0.26241
## 354 524 1 0.7362 0.26382
## 357 522 1 0.7348 0.26523
## 359 521 1 0.7334 0.26664
## 362 520 1 0.7320 0.26805
## 363 519 1 0.7305 0.26946
## 364 518 1 0.7291 0.27087
## 370 517 1 0.7277 0.27228
## 372 515 1 0.7263 0.27369
## 374 514 1 0.7249 0.27510
## 379 511 1 0.7235 0.27652
## 380 510 2 0.7206 0.27936
## 387 506 1 0.7192 0.28078
## 388 504 2 0.7164 0.28364
## 389 501 3 0.7121 0.28793
## 391 497 1 0.7106 0.28936
## 392 496 1 0.7092 0.29079
## 393 495 1 0.7078 0.29222
## 394 494 1 0.7063 0.29366
## 402 492 1 0.7049 0.29509
## 403 491 1 0.7035 0.29653
## 406 487 1 0.7020 0.29797
## 407 485 1 0.7006 0.29942
## 416 478 1 0.6991 0.30089
## 420 473 1 0.6976 0.30236
## 421 472 3 0.6932 0.30680
## 425 468 1 0.6917 0.30828
## 429 466 1 0.6902 0.30976
## 435 460 1 0.6887 0.31126
## 440 458 1 0.6872 0.31277
## 441 457 1 0.6857 0.31427
## 442 455 3 0.6812 0.31879
## 443 451 1 0.6797 0.32030
## 444 450 1 0.6782 0.32181
## 445 449 1 0.6767 0.32332
## 447 446 1 0.6752 0.32484
## 449 445 2 0.6721 0.32788
## 451 441 2 0.6691 0.33092
## 452 439 1 0.6676 0.33245
## 455 438 2 0.6645 0.33550
## 459 434 1 0.6630 0.33703
## 463 433 1 0.6614 0.33856
## 464 432 1 0.6599 0.34009
## 465 431 1 0.6584 0.34162
## 466 429 1 0.6568 0.34316
## 468 427 1 0.6553 0.34469
## 472 424 1 0.6538 0.34624
## 474 421 1 0.6522 0.34779
## 477 419 1 0.6507 0.34935
## 488 416 1 0.6491 0.35091
## 489 413 3 0.6444 0.35563
## 490 410 1 0.6428 0.35720
## 492 405 1 0.6412 0.35879
## 494 404 1 0.6396 0.36037
## 499 399 1 0.6380 0.36198
## 501 395 1 0.6364 0.36359
## 506 391 1 0.6348 0.36522
## 508 389 3 0.6299 0.37012
## 509 386 3 0.6250 0.37501
## 510 383 1 0.6234 0.37664
## 512 382 2 0.6201 0.37991
## 514 379 1 0.6185 0.38154
## 517 377 2 0.6152 0.38482
## 518 374 1 0.6135 0.38647
## 521 373 2 0.6102 0.38976
## 525 371 1 0.6086 0.39140
## 529 369 1 0.6069 0.39305
## 530 368 1 0.6053 0.39470
## 544 358 1 0.6036 0.39639
## 545 357 2 0.6002 0.39977
## 553 352 1 0.5985 0.40148
## 555 351 2 0.5951 0.40489
## 558 348 1 0.5934 0.40660
## 560 346 1 0.5917 0.40831
## 563 344 1 0.5900 0.41003
## 565 342 1 0.5882 0.41176
## 566 341 2 0.5848 0.41521
## 567 339 1 0.5831 0.41694
## 570 337 1 0.5813 0.41867
## 571 336 2 0.5779 0.42213
## 580 329 1 0.5761 0.42388
## 581 328 1 0.5744 0.42564
## 588 323 1 0.5726 0.42742
## 589 322 1 0.5708 0.42919
## 593 318 1 0.5690 0.43099
## 594 317 1 0.5672 0.43278
## 599 315 1 0.5654 0.43459
## 608 311 1 0.5636 0.43640
## 611 310 1 0.5618 0.43822
## 612 309 1 0.5600 0.44004
## 621 306 2 0.5563 0.44370
## 625 303 1 0.5545 0.44554
## 628 301 1 0.5526 0.44738
## 630 300 1 0.5508 0.44922
## 631 299 2 0.5471 0.45290
## 632 297 2 0.5434 0.45659
## 634 292 1 0.5416 0.45845
## 638 291 1 0.5397 0.46031
## 640 290 1 0.5378 0.46217
## 643 289 1 0.5360 0.46403
## 644 288 2 0.5322 0.46775
## 649 285 1 0.5304 0.46962
## 650 283 1 0.5285 0.47150
## 651 282 1 0.5266 0.47337
## 653 279 1 0.5247 0.47526
## 654 278 1 0.5229 0.47714
## 656 277 1 0.5210 0.47903
## 658 276 1 0.5191 0.48092
## 659 274 1 0.5172 0.48281
## 663 272 1 0.5153 0.48472
## 665 266 1 0.5133 0.48665
## 668 263 2 0.5094 0.49056
## 669 259 1 0.5075 0.49252
## 670 258 1 0.5055 0.49449
## 674 255 1 0.5035 0.49647
## 675 254 1 0.5015 0.49846
## 677 253 3 0.4956 0.50440
## 684 249 1 0.4936 0.50639
## 685 246 1 0.4916 0.50840
## 687 245 2 0.4876 0.51241
## 693 241 2 0.4835 0.51646
## 696 238 1 0.4815 0.51849
## 699 233 1 0.4794 0.52056
## 701 231 1 0.4774 0.52263
## 703 229 1 0.4753 0.52472
## 709 228 1 0.4732 0.52680
## 714 227 1 0.4711 0.52889
## 719 221 1 0.4690 0.53102
## 734 213 1 0.4668 0.53322
## 739 212 2 0.4624 0.53762
## 740 209 3 0.4557 0.54426
## 749 199 1 0.4534 0.54655
## 751 198 1 0.4512 0.54884
## 752 197 1 0.4489 0.55113
## 757 195 1 0.4466 0.55343
## 758 192 1 0.4442 0.55576
## 764 191 1 0.4419 0.55808
## 765 190 1 0.4396 0.56041
## 767 189 1 0.4373 0.56274
## 768 185 1 0.4349 0.56510
## 771 182 2 0.4301 0.56988
## 773 179 2 0.4253 0.57468
## 778 174 1 0.4229 0.57713
## 787 173 1 0.4204 0.57957
## 791 172 1 0.4180 0.58202
## 793 171 1 0.4155 0.58446
## 795 170 1 0.4131 0.58691
## 796 169 1 0.4106 0.58935
## 802 164 2 0.4056 0.59436
## 810 157 1 0.4031 0.59694
## 814 156 1 0.4005 0.59953
## 815 155 1 0.3979 0.60211
## 819 154 1 0.3953 0.60469
## 822 152 1 0.3927 0.60729
## 830 148 1 0.3901 0.60995
## 833 146 1 0.3874 0.61262
## 834 145 1 0.3847 0.61529
## 837 144 1 0.3820 0.61796
## 845 139 1 0.3793 0.62071
## 846 138 1 0.3765 0.62346
## 858 134 1 0.3737 0.62627
## 863 132 1 0.3709 0.62910
## 865 129 1 0.3680 0.63198
## 867 127 1 0.3651 0.63487
## 870 126 1 0.3622 0.63777
## 876 123 1 0.3593 0.64072
## 877 122 1 0.3563 0.64366
## 878 120 1 0.3534 0.64663
## 884 117 1 0.3503 0.64965
## 889 113 1 0.3472 0.65275
## 892 112 1 0.3441 0.65585
## 893 111 1 0.3410 0.65895
## 894 109 2 0.3348 0.66521
## 895 107 1 0.3317 0.66834
## 901 106 1 0.3285 0.67147
## 908 103 1 0.3253 0.67466
## 912 100 1 0.3221 0.67791
## 913 99 1 0.3188 0.68116
## 915 98 1 0.3156 0.68442
## 918 97 1 0.3123 0.68767
## 922 95 2 0.3058 0.69425
## 931 92 1 0.3024 0.69757
## 937 88 1 0.2990 0.70101
## 939 87 1 0.2956 0.70444
## 950 81 2 0.2883 0.71174
## 955 79 1 0.2846 0.71539
## 957 77 1 0.2809 0.71909
## 960 75 1 0.2772 0.72283
## 971 72 1 0.2733 0.72668
## 975 71 1 0.2695 0.73053
## 983 66 1 0.2654 0.73461
## 984 63 1 0.2612 0.73883
## 994 58 1 0.2567 0.74333
## 995 57 1 0.2522 0.74783
## 1004 54 1 0.2475 0.75250
## 1006 53 1 0.2428 0.75717
## 1013 51 1 0.2381 0.76193
## 1020 47 1 0.2330 0.76700
## 1028 45 2 0.2226 0.77735
## 1029 43 1 0.2175 0.78253
## 1030 42 1 0.2123 0.78771
## 1040 39 1 0.2068 0.79315
## 1043 36 2 0.1954 0.80464
## 1045 34 1 0.1896 0.81039
## 1047 33 1 0.1839 0.81614
## 1048 31 2 0.1720 0.82800
## 1052 27 1 0.1656 0.83437
## 1057 26 1 0.1593 0.84074
## 1059 24 1 0.1526 0.84737
## 1062 22 1 0.1457 0.85431
## 1063 19 1 0.1380 0.86198
## 1067 16 1 0.1294 0.87061
## 1072 14 1 0.1202 0.87985
## 1080 12 1 0.1101 0.88986
## 1085 10 1 0.0991 0.90088
## 1086 9 1 0.0881 0.91189
## 1100 3 1 0.0587 0.94126
## 1107 1 1 0.0000 1.00000
##
## age_cat=Below 65
## time n.risk n.event P((s0)) P(1)
## 18 123 1 0.992 0.00813
## 32 118 1 0.983 0.01654
## 63 115 1 0.975 0.02509
## 149 110 1 0.966 0.03395
## 156 109 1 0.957 0.04281
## 161 108 1 0.948 0.05168
## 383 99 2 0.929 0.07083
## 452 84 1 0.918 0.08190
## 460 82 1 0.907 0.09309
## 510 77 1 0.895 0.10487
## 515 75 1 0.883 0.11681
## 533 71 1 0.871 0.12924
## 562 67 1 0.858 0.14224
## 705 52 1 0.841 0.15874
## 730 48 1 0.824 0.17626
## 790 36 1 0.801 0.19914
## 793 35 1 0.778 0.22203
## 841 27 1 0.749 0.25084
## 989 14 1 0.696 0.30435
## 1019 10 1 0.626 0.37392
## 1041 7 1 0.537 0.46336
## 1085 3 1 0.358 0.64224Log rank test with age group
survdiff(Surv(fu_time,death)~age_cat,data = g)## Call:
## survdiff(formula = Surv(fu_time, death) ~ age_cat, data = g)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## age_cat=Above 65 869 469 415.4 6.9 44.6
## age_cat=Below 65 131 23 76.6 37.5 44.6
##
## Chisq= 44.6 on 1 degrees of freedom, p= 2e-11Scientific notation removal
format(2e-11 , scientific=F, digits=3)## [1] "0.00000000002"COX survival analysis
library(survival)
library(ggplot2)
library(ggpubr)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v tibble 3.1.7 v dplyr 1.0.9
## v tidyr 1.2.0 v stringr 1.4.0
## v purrr 0.3.4 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(survminer)
cox=coxph(Surv(fu_time,death)~age, data = g)
summary(cox)## Call:
## coxph(formula = Surv(fu_time, death) ~ age, data = g)
##
## n= 1000, number of events= 492
##
## coef exp(coef) se(coef) z Pr(>|z|)
## age 0.056005 1.057602 0.005193 10.78 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## age 1.058 0.9455 1.047 1.068
##
## Concordance= 0.651 (se = 0.013 )
## Likelihood ratio test= 138 on 1 df, p=<2e-16
## Wald test = 116.3 on 1 df, p=<2e-16
## Score (logrank) test = 115.7 on 1 df, p=<2e-16head(g)## id death los age gender cancer cabg crt defib dementia diabetes hypertension
## 1 1 0 2 90 2 0 0 0 0 0 0 0
## 2 2 0 10 74 1 0 0 0 0 0 0 1
## 3 3 0 3 83 2 0 0 0 0 0 0 1
## 4 4 0 1 79 1 0 0 0 0 0 1 1
## 5 5 0 17 94 2 0 0 0 0 0 1 1
## 6 6 0 47 89 1 0 0 0 0 0 0 0
## ihd mental_health arrhythmias copd obesity pvd renal_disease valvular_disease
## 1 0 0 1 0 0 0 0 1
## 2 1 0 0 0 0 0 1 1
## 3 0 0 1 0 0 0 0 0
## 4 1 0 0 1 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 1 0 1
## metastatic_cancer pacemaker pneumonia prior_appts_attended prior_dnas pci
## 1 0 0 0 4 0 0
## 2 0 0 1 9 1 0
## 3 0 0 0 1 0 0
## 4 0 1 0 9 2 1
## 5 0 0 0 3 0 0
## 6 0 0 1 3 0 0
## stroke senile quintile ethnicgroup fu_time
## 1 0 0 2 NA 416
## 2 0 0 4 1 648
## 3 0 0 3 1 466
## 4 1 0 5 1 441
## 5 0 0 2 1 371
## 6 0 0 3 NA 47ethnicgroup=as.factor(g[,"ethnicgroup"])Cox regression with ethnicgroup
cox_ethnicgroup=coxph(Surv(fu_time,death)~ethnicgroup, data = g)
summary(cox_ethnicgroup)## Call:
## coxph(formula = Surv(fu_time, death) ~ ethnicgroup, data = g)
##
## n= 957, number of events= 471
## (43 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## ethnicgroup -0.04555 0.95547 0.05069 -0.899 0.369
##
## exp(coef) exp(-coef) lower .95 upper .95
## ethnicgroup 0.9555 1.047 0.8651 1.055
##
## Concordance= 0.514 (se = 0.006 )
## Likelihood ratio test= 0.89 on 1 df, p=0.3
## Wald test = 0.81 on 1 df, p=0.4
## Score (logrank) test = 0.81 on 1 df, p=0.4